The coloc package can be used to perform genetic colocalisation analysis of two potentially related phenotypes, to ask whether they share common genetic causal variant(s) in a given region.
This update (version 5) supercedes previously published version 4 by introducing use of the SuSiE approach to deal with multiple causal variants rather than conditioning or masking. See
Wang, G., Sarkar, A., Carbonetto, P., & Stephens, M. (2020). A simple new approach to variable selection in regression, with application to genetic fine mapping. Journal of the Royal Statistical Society: Series B (Statistical Methodology). https://doi.org/10.1111/rssb.12388
for the full SuSiE paper and
Wallace (2021). A more accurate method for colocalisation analysis allowing for multiple causal variants. bioRxiv https://www.biorxiv.org/content/10.1101/2021.02.23.432421v1
for a description of its use in coloc.
To install from R, do
if(!require("remotes")) install.packages("remotes") # if necessary library(remotes) install_github("chr1swallace/coloc",build_vignettes=TRUE)
Note that in all simulations, susie outperforms the earlier conditioning approach, so is recommended. However, it is also new code, so please consider the code “beta” and let me know of any issues that arise - they may be a bug on my part. If you want to use it, the function you want to look at is
coloc.susie. It can take raw datasets, but the time consuming part is running SuSiE. coloc runs SuSiE and saves a little extra information using the
runsusie function before running an adapted colocalisation on the results. So please look at the docs for
runsusie too. I found a helpful recipe is
runsusieon dataset 1, storing the results
runsusieon dataset 2, storing the results
coloc.susieon the two outputs from above
More detail is available in the vignette a06_SuSiE.html accessible by
For usage, please see the vignette at https://chr1swallace.github.io/coloc
Key previous references are:
to generate website: https://chr1swallace.github.io/coloc/
Rscript -e "pkgdown::build_site()"